The Continuing Importance of Transrectal Ultrasound Identification of Prostatic Lesions
Bibliographic record
Abstract
PURPOSE: In light of a recent tendency toward systematic nontargeted biopsy we reassessed whether identification and biopsy of ultrasonographically suspicious lesions contribute to the detection of prostate cancer. MATERIALS AND METHODS: We reviewed prospectively gathered data on 7,426 transrectal ultrasound directed prostatic biopsies performed at our institution between June 16, 2000 and September 1, 2005. Patients underwent systematic biopsy (6 to 10 cores on initial biopsy and 13 to 15 on rebiopsy) with additional sampling of visible suspicious lesions. The RR for finding cancer in transrectal ultrasound positive and negative patients was calculated for likely independent prognostic variables. RESULTS: A total of 3,828 biopsies (51.5%) were transrectal ultrasound negative and 3,598 (48.5%) were transrectal ultrasound positive. Prostate cancer was detected in 3,258 biopsies (43.9%). For each independent variable the RR for prostate cancer was higher if a sonographic lesion was present. A lesion increased the likelihood of cancer detection (57.8% vs 30.8%, RR 1.8). Biopsies from lesions identified by transrectal ultrasound had a greater median percent of the core involved with cancer (50% vs 10%, p <0.001) and they were more likely to have Gleason score 7 or greater (69.3% vs 28.3%, p <0.001). CONCLUSIONS: Biopsies taken when a prostatic lesion is identified by transrectal ultrasound are almost twice as likely to show cancer than when no lesion is visible. These cancers are of higher grade and volume and, therefore, they are more clinically significant. The search for and targeted biopsy of suspicious lesions seen on transrectal ultrasound remains important for prostate cancer diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".